What is an AI agent?
An AI agent is an AI system with a goal and a loop: it plans, produces an output, checks its work, and iterates. This page explains the basics, then shows when one agent is enough and when an AI agent swarm is the better fit.
What is an AI agent?
In practice, an AI agent is a model wrapped with a role, constraints, and a predictable interface. It might have access to tools (APIs), memory, and guardrails — but the core idea is simple: the agent takes a goal and returns work in a specific format.
AI agent vs AI agent swarm
| Pattern | Best for | Tradeoff |
|---|---|---|
| One AI agent | Fast rewrites, short answers, and simple one-step tasks. | Less built-in critique and less coverage when the task has edge cases. |
| AI agent swarm | Plans, specs, SOPs, research summaries, and other deliverables that need review. | More overhead, so it only pays off when the final artifact matters. |
| Human + agent review | High-risk work where correctness matters more than speed. | Still requires explicit validation before anything ships. |
The agent loop (plan → act → check)
Most “agentic” systems are just loops. The loop can be explicit (separate stages) or implicit, but reliable agents usually:
- Plan: decide what to do and what the output should look like.
- Act: produce the draft output (or call tools, when configured).
- Check: verify constraints, find gaps, and fix the top issues.
Common agent roles (and why they work)
Reliable agent systems use narrow roles. Narrow roles make it easier to verify outputs and reduce the chance that one “generalist” response misses key steps. In SwarmLM, a typical swarm run uses:
Planner
Defines constraints, decomposes the task, and chooses a deliverable format.
Creator
Produces the primary draft artifact from the plan.
Reviewer
Finds gaps, contradictions, missing edge cases, and unclear parts.
Integrator
Merges improvements into a clean final deliverable ready to export.
Interfaces: make agent outputs testable
The fastest way to improve agent quality is to make outputs easy to test. Prefer structured formats (clear headings, checklists, JSON) over vague prose. You want a human (or a script) to be able to answer: “Did the agent follow the spec?”
How to design agents that produce reliable work
Agent spec template
Role: (planner / creator / reviewer / integrator)
Inputs: (task, constraints, context)
Output format: (markdown / checklist / JSON / code scaffold)
Definition of done: (what must be true for success)
Failure modes: (what to avoid)
Verification: (how a human can quickly check correctness)
If you can’t write down the role and output format in one paragraph, the agent is likely too broad. Tighten the role, add constraints, and make the interface testable.
Evaluation: how you know an agent is “good”
Don’t judge agents only by “vibes”. Pick a small set of test tasks and measure simple outcomes: does the output match the requested format, does it include a definition of done, are constraints preserved, and is the next action executable?
Practical metrics
- Format compliance (pass/fail)
- Constraint compliance (pass/fail)
- Missing-edge-case count (review catches issues)
- Time-to-first-action (minutes)
- Cost (if using paid providers)
When to use a swarm of agents
Agent swarms are most useful when:
- The task is large and benefits from decomposition.
- You need built-in critique (review) before you ship the output.
- You want a deliverable with steps, risks, and a clear “definition of done”.
Connect the dots: Swarm AI describes the multi-agent pattern, and
Swarm intelligence covers the principles behind it. The
API docs show how those ideas map to the public demo and paid-run endpoints.
Try AI agent swarms with SwarmLM
SwarmLM is built to produce artifacts you can execute. Run a demo swarm and download the result as Markdown: Try the demo or inspect the API docs.